Computational prediction of miRNA genes from small RNA sequencing data

• Main Authors: Wenjing eKang, Marc Riemer Friedländer
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-01-01
• Series: Frontiers in Bioengineering and Biotechnology
• Subjects: microRNA; miRNA; gene prediction; next-generation sequencing data; Small RNA sequencing
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fbioe.2015.00007/full

Next-generation sequencing now for the first time allows researchers to gauge the depth and variation of entire transcriptomes. However, now as rare transcripts can be detected that are present in cells at single copies, more advanced computational tools are needed to accurately annotate and profile them. miRNAs are 22 nucleotide small RNAs (sRNAs) that post-transcriptionally reduce the output of protein coding genes. They have established roles in numerous biological processes, including cancers and other diseases. During miRNA biogenesis, the sRNAs are sequentially cleaved from precursor molecules that have a characteristic hairpin RNA structure. The vast majority of new miRNA genes that are discovered are mined from small RNA sequencing (sRNA-seq), which can detect more than a billion RNAs in a single run. However, given that many of the detected RNAs are degradation products from all types of transcripts, the accurate identification of miRNAs remain a non-trivial computational problem. Here we review the tools available to predict animal miRNAs from sRNA sequencing data. We present tools for generalist and specialist use cases, including prediction from massively pooled data or in species without reference genome. We also present wet-lab methods used to validate predicted miRNAs, and approaches to computationally benchmark prediction accuracy. For each tool, we reference validation experiments and benchmarking efforts. Last, we discuss the future of the field.